用注意力机制实现中英文互译

[KEY: > input, = target, < output]

il est en train de peindre un tableau .
= he is painting a picture .
< he is painting a picture .

pourquoi ne pas essayer ce vin delicieux ?
= why not try that delicious wine ?
< why not try that delicious wine ?

elle n est pas poete mais romanciere .
= she is not a poet but a novelist .
< she not not a poet but a novelist .

导入需要的模块及数据

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from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata
import string
import re
import random
import jieba
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F

import matplotlib.font_manager as fm
myfont = fm.FontProperties(fname='/Users/maqi/opt/anaconda3/lib/python3.8/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf')

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

预处理数据

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SOS_token = 0
EOS_token = 1


class Lang:
    def __init__(self, name):
        self.name = name
        self.word2index = {}
        self.word2count = {}
        self.index2word = {0: "SOS", 1: "EOS"}
        self.n_words = 2  # Count SOS and EOS

    def addSentence(self, sentence):
        for word in sentence.split(' '):
            self.addWord(word)
    def addSentence_cn(self, sentence):
        for word in list(jieba.cut(sentence)):
            self.addWord(word)

    def addWord(self, word):
        if word not in self.word2index:
            self.word2index[word] = self.n_words
            self.word2count[word] = 1
            self.index2word[self.n_words] = word
            self.n_words += 1
        else:
            self.word2count[word] += 1
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# 为便于数据处理，把Unicode字符串转换为ASCII编码

def unicodeToAscii(s):
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn'
    )

# 对英文转换为小写，去空格及非字母符号等处理

def normalizeString(s):
    s = unicodeToAscii(s.lower().strip())
    s = re.sub(r"([.!?])", r" \1", s)
    #s = re.sub(r"[^a-zA-Z.!?]+", r" ", s)
    return s
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def readLangs(lang1, lang2, reverse=False):
    print("Reading lines...")

    # 读文件，然后分成行
    lines = open('eng-cmn/%s-%s.txt' % (lang1, lang2), encoding='utf-8').\
        read().strip().split('\n')

    # 把行分成语句对，并进行规范化
    pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]

    # 判断是否需要转换语句对的次序，如[英文，中文]转换为[中文，英文]次序
    if reverse:
        pairs = [list(reversed(p)) for p in pairs]
        input_lang = Lang(lang2)
        output_lang = Lang(lang1)
    else:
        input_lang = Lang(lang1)
        output_lang = Lang(lang2)

    return input_lang, output_lang, pairs
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#为便于训练，这里选择部分数据
MAX_LENGTH = 20

eng_prefixes = (
    "i am ", "i m ",
    "he is", "he s ",
    "she is", "she s ",
    "you are", "you re ",
    "we are", "we re ",
    "they are", "they re "
)


def filterPair(p):
    return len(p[0].split(' ')) < MAX_LENGTH and \
        len(p[1].split(' ')) < MAX_LENGTH and \
        p[1].startswith(eng_prefixes)


def filterPairs(pairs):
    return [pair for pair in pairs if filterPair(pair)]
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def prepareData(lang1, lang2, reverse=False):
    input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse)
    print("Read %s sentence pairs" % len(pairs))
    pairs = filterPairs(pairs)
    print("Trimmed to %s sentence pairs" % len(pairs))
    print("Counting words...")
    for pair in pairs:
        input_lang.addSentence_cn(pair[0])
        output_lang.addSentence(pair[1])
    print("Counted words:")
    print(input_lang.name, input_lang.n_words)
    print(output_lang.name, output_lang.n_words)
    return input_lang, output_lang, pairs
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input_lang, output_lang, pairs = prepareData('eng', 'cmn',True)
print(random.choice(pairs))
Reading lines...


Building prefix dict from the default dictionary ...
Loading model from cache /var/folders/7t/wvjcfn5575g892qb2nqbd9kw0000gn/T/jieba.cache


Read 21007 sentence pairs
Trimmed to 640 sentence pairs
Counting words...


Loading model cost 0.571 seconds.
Prefix dict has been built succesfully.


Counted words:
cmn 1063
eng 808
['他很穷。', 'he is poor .']

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pairs[:3]
[['我冷。', 'i am cold .'], ['我沒事。', 'i am okay .'], ['我生病了。', 'i am sick .']]

构建模型

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class EncoderRNN(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(EncoderRNN, self).__init__()
        self.hidden_size = hidden_size

        self.embedding = nn.Embedding(input_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size)

    def forward(self, input, hidden):
        embedded = self.embedding(input).view(1, 1, -1)
        output = embedded
        output, hidden = self.gru(output, hidden)
        return output, hidden

    def initHidden(self):
        return torch.zeros(1, 1, self.hidden_size, device=device)
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class DecoderRNN(nn.Module):
    def __init__(self, hidden_size, output_size):
        super(DecoderRNN, self).__init__()
        self.hidden_size = hidden_size

        self.embedding = nn.Embedding(output_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size)
        self.out = nn.Linear(hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, input, hidden):
        output = self.embedding(input).view(1, 1, -1)
        output = F.relu(output)
        output, hidden = self.gru(output, hidden)
        output = self.softmax(self.out(output[0]))
        return output, hidden

    def initHidden(self):
        return torch.zeros(1, 1, self.hidden_size, device=device)
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class AttnDecoderRNN(nn.Module):
    def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):
        super(AttnDecoderRNN, self).__init__()
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.dropout_p = dropout_p
        self.max_length = max_length

        self.embedding = nn.Embedding(self.output_size, self.hidden_size)
        self.attn = nn.Linear(self.hidden_size * 2, self.max_length)
        self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)
        self.dropout = nn.Dropout(self.dropout_p)
        self.gru = nn.GRU(self.hidden_size, self.hidden_size)
        self.out = nn.Linear(self.hidden_size, self.output_size)

    def forward(self, input, hidden, encoder_outputs):
        embedded = self.embedding(input).view(1, 1, -1)
        embedded = self.dropout(embedded)

        attn_weights = F.softmax(
            self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)
        attn_applied = torch.bmm(attn_weights.unsqueeze(0),
                                 encoder_outputs.unsqueeze(0))

        output = torch.cat((embedded[0], attn_applied[0]), 1)
        output = self.attn_combine(output).unsqueeze(0)

        output = F.relu(output)
        output, hidden = self.gru(output, hidden)

        output = F.log_softmax(self.out(output[0]), dim=1)
        return output, hidden, attn_weights

    def initHidden(self):
        return torch.zeros(1, 1, self.hidden_size, device=device)
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def indexesFromSentence(lang, sentence):
    return [lang.word2index[word] for word in sentence.split(' ')]

def indexesFromSentence_cn(lang, sentence):
    return [lang.word2index[word] for word in list(jieba.cut(sentence))]


def tensorFromSentence(lang, sentence):
    indexes = indexesFromSentence(lang, sentence)
    indexes.append(EOS_token)
    return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)

def tensorFromSentence_cn(lang, sentence):
    indexes = indexesFromSentence_cn(lang, sentence)
    indexes.append(EOS_token)
    return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)


def tensorsFromPair(pair):
    input_tensor = tensorFromSentence_cn(input_lang, pair[0])
    target_tensor = tensorFromSentence(output_lang, pair[1])
    return (input_tensor, target_tensor)

训练模型

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teacher_forcing_ratio = 0.5


def train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length=MAX_LENGTH):
    encoder_hidden = encoder.initHidden()

    encoder_optimizer.zero_grad()
    decoder_optimizer.zero_grad()

    input_length = input_tensor.size(0)
    target_length = target_tensor.size(0)

    encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)

    loss = 0

    for ei in range(input_length):
        encoder_output, encoder_hidden = encoder(
            input_tensor[ei], encoder_hidden)
        encoder_outputs[ei] = encoder_output[0, 0]

    decoder_input = torch.tensor([[SOS_token]], device=device)

    decoder_hidden = encoder_hidden

    use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False

    if use_teacher_forcing:
        # Teacher forcing: Feed the target as the next input
        for di in range(target_length):
            decoder_output, decoder_hidden, decoder_attention = decoder(
                decoder_input, decoder_hidden, encoder_outputs)
            loss += criterion(decoder_output, target_tensor[di])
            decoder_input = target_tensor[di]  # Teacher forcing

    else:
        # Without teacher forcing: use its own predictions as the next input
        for di in range(target_length):
            decoder_output, decoder_hidden, decoder_attention = decoder(
                decoder_input, decoder_hidden, encoder_outputs)
            topv, topi = decoder_output.topk(1)
            decoder_input = topi.squeeze().detach()  # detach from history as input

            loss += criterion(decoder_output, target_tensor[di])
            if decoder_input.item() == EOS_token:
                break

    loss.backward()

    encoder_optimizer.step()
    decoder_optimizer.step()

    return loss.item() / target_length
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import time
import math


def asMinutes(s):
    m = math.floor(s / 60)
    s -= m * 60
    return '%dm %ds' % (m, s)


def timeSince(since, percent):
    now = time.time()
    s = now - since
    es = s / (percent)
    rs = es - s
    return '%s (- %s)' % (asMinutes(s), asMinutes(rs))
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def trainIters(encoder, decoder, n_iters, print_every=1000, plot_every=100, learning_rate=0.01):
    start = time.time()
    plot_losses = []
    print_loss_total = 0  
    plot_loss_total = 0 

    encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
    decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)
    training_pairs = [tensorsFromPair(random.choice(pairs))
                      for i in range(n_iters)]
    criterion = nn.NLLLoss()

    for iter in range(1, n_iters + 1):
        training_pair = training_pairs[iter - 1]
        input_tensor = training_pair[0]
        target_tensor = training_pair[1]

        loss = train(input_tensor, target_tensor, encoder,
                     decoder, encoder_optimizer, decoder_optimizer, criterion)
        print_loss_total += loss
        plot_loss_total += loss

        if iter % print_every == 0:
            print_loss_avg = print_loss_total / print_every
            print_loss_total = 0
            print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters),
                                         iter, iter / n_iters * 100, print_loss_avg))

        if iter % plot_every == 0:
            plot_loss_avg = plot_loss_total / plot_every
            plot_losses.append(plot_loss_avg)
            plot_loss_total = 0

    showPlot(plot_losses)
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import matplotlib.pyplot as plt
%matplotlib inline

#plt.switch_backend('agg')
import matplotlib.ticker as ticker
import numpy as np


def showPlot(points):
    plt.figure()
    fig, ax = plt.subplots()
    # this locator puts ticks at regular intervals
    loc = ticker.MultipleLocator(base=0.2)
    ax.yaxis.set_major_locator(loc)
    plt.plot(points)
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def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):
    with torch.no_grad():
        input_tensor = tensorFromSentence_cn(input_lang, sentence)
        input_length = input_tensor.size()[0]
        encoder_hidden = encoder.initHidden()

        encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)

        for ei in range(input_length):
            encoder_output, encoder_hidden = encoder(input_tensor[ei],
                                                     encoder_hidden)
            encoder_outputs[ei] += encoder_output[0, 0]

        decoder_input = torch.tensor([[SOS_token]], device=device)  # SOS

        decoder_hidden = encoder_hidden

        decoded_words = []
        decoder_attentions = torch.zeros(max_length, max_length)

        for di in range(max_length):
            decoder_output, decoder_hidden, decoder_attention = decoder(
                decoder_input, decoder_hidden, encoder_outputs)
            decoder_attentions[di] = decoder_attention.data
            topv, topi = decoder_output.data.topk(1)
            if topi.item() == EOS_token:
                decoded_words.append('<EOS>')
                break
            else:
                decoded_words.append(output_lang.index2word[topi.item()])

            decoder_input = topi.squeeze().detach()

        return decoded_words, decoder_attentions[:di + 1]
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def evaluateRandomly(encoder, decoder, n=10):
    for i in range(n):
        pair = random.choice(pairs)
        print('>', pair[0])
        print('=', pair[1])
        output_words, attentions = evaluate(encoder, decoder, pair[0])
        output_sentence = ' '.join(output_words)
        print('<', output_sentence)
        print('')
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hidden_size = 256
encoder1 = EncoderRNN(input_lang.n_words, hidden_size).to(device)
attn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words, dropout_p=0.1).to(device)

trainIters(encoder1, attn_decoder1, 75000, print_every=5000)
1m 54s (- 26m 36s) (5000 6%) 2.6394
3m 43s (- 24m 10s) (10000 13%) 1.0916
5m 34s (- 22m 19s) (15000 20%) 0.2057
7m 29s (- 20m 36s) (20000 26%) 0.0445
9m 27s (- 18m 54s) (25000 33%) 0.0253
11m 25s (- 17m 7s) (30000 40%) 0.0202
13m 20s (- 15m 14s) (35000 46%) 0.0175
15m 17s (- 13m 23s) (40000 53%) 0.0167
17m 15s (- 11m 30s) (45000 60%) 0.0141
19m 13s (- 9m 36s) (50000 66%) 0.0137
21m 12s (- 7m 42s) (55000 73%) 0.0110
23m 12s (- 5m 48s) (60000 80%) 0.0116
25m 12s (- 3m 52s) (65000 86%) 0.0125
27m 11s (- 1m 56s) (70000 93%) 0.0091
29m 11s (- 0m 0s) (75000 100%) 0.0095



<Figure size 432x288 with 0 Axes>

png

随机采样，对模型进行测试

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evaluateRandomly(encoder1, attn_decoder1)
> 今天下午我會外出。
= i am going out this afternoon .
< i am going out this afternoon . <EOS>

> 我相信他是無辜的。
= i am convinced that he is innocent .
< i am convinced that he is innocent . <EOS>

> 他在自己房里玩。
= he is playing in his room .
< he is playing in his room . <EOS>

> 我來自四國。
= i am from shikoku .
< i am from shikoku . <EOS>

> 她戴著一頂帽子。
= she is wearing a hat .
< she is wearing a hat . <EOS>

> 您非常勇敢。
= you are very courageous .
< you are very brave . <EOS>

> 他有几分像学者。
= he is something of a scholar .
< he is something of a scholar . <EOS>

> 你真傻。
= you are so stupid .
< you are so stupid . <EOS>

> 他年紀夠大可以瞭解它。
= he is old enough to understand it .
< he is old enough to understand it . <EOS>

> 你別小看了他。
= you are selling him short .
< you are selling him short . <EOS>

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def evaluate_randomly():
    pair = random.choice(pairs)
    
    output_words, decoder_attn = evaluate(pair[0])
    output_sentence = ' '.join(output_words)
    
    print('>', pair[0])
    print('=', pair[1])
    print('<', output_sentence)
    print('')
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def evaluateRandomly(encoder, decoder, n=20):
    for i in range(n):
        pair = random.choice(pairs)
        print('>', pair[0])
        print('=', pair[1])
        output_words, attentions = evaluate(encoder, decoder, pair[0])
        output_sentence = ' '.join(output_words)
        print('<', output_sentence)
        print('')

可视化注意力

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def showAttention(input_sentence, output_words, attentions):
    # Set up figure with colorbar
    fig = plt.figure()
    ax = fig.add_subplot(111)
    cax = ax.matshow(attentions.numpy(), cmap='bone')
    fig.colorbar(cax)

    # Set up axes
    ax.set_xticklabels([''] + list(jieba.cut(input_sentence)) +
                       ['<EOS>'], rotation=90,fontproperties=myfont)
    ax.set_yticklabels([''] + output_words)

    # Show label at every tick
    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))

    plt.show()
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def evaluateAndShowAttention(input_sentence):
    output_words, attentions = evaluate(
        encoder1, attn_decoder1, input_sentence)
    print('input =', input_sentence)
    print('output =', ' '.join(output_words))
    showAttention(input_sentence, output_words, attentions)


evaluateAndShowAttention("我很幸福。")

evaluateAndShowAttention("我们在严肃地谈论你的未来。")

evaluateAndShowAttention("我在家。")

evaluateAndShowAttention("我们在严肃地谈论你的未来。")
input = 我很幸福。
output = i am very happy . <EOS>


<ipython-input-23-2d6791f485ef>:9: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_xticklabels([''] + list(jieba.cut(input_sentence)) +
<ipython-input-23-2d6791f485ef>:11: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_yticklabels([''] + output_words)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 25105 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 24456 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 24184 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 31119 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 12290 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 25105 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 24456 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 24184 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 31119 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 12290 missing from current font.
  font.set_text(s, 0, flags=flags)

png

input = 我们在严肃地谈论你的未来。
output = we are having a serious talk about your future . <EOS>


/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 20204 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 22312 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 20005 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 32899 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 22320 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 35848 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 35770 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 20320 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 30340 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 26410 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 26469 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 20204 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 22312 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 20005 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 32899 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 22320 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 35848 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 35770 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 20320 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 30340 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 26410 missing from current font.
  font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 26469 missing from current font.
  font.set_text(s, 0, flags=flags)

png

input = 我在家。
output = i am at home . <EOS>


/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 23478 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/mq_env/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 23478 missing from current font.
  font.set_text(s, 0, flags=flags)

png

input = 我们在严肃地谈论你的未来。
output = we are having a serious talk about your future . <EOS>

png

1